Category: Amazon Aurora

In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.

GaadiBazaar draws on over 25 years of vehicle finance expertise from Cholamandalam to connect vehicle buyers and sellers. Their mission is to enable hassle-free transactions at fair prices through buyer-seller interactions and end-to-end financial assistance. This post shows you how GaadiBazaar, an online platform for buying and selling vehicles, achieved significant database cost savings by migrating to Amazon Aurora MySQL Compatible Edition Serverless.

In this post, we look into Kerberos authentication for Amazon Aurora PostgreSQL-Compatible Edition using AWS Directory Service for Microsoft Active Directory, and particularly the new pg_ad_mapping extension and how it can help you manage access control more efficiently.

Aqua Security is the pioneer in securing containerized cloud native applications from development to production. Like many organizations, Aqua faced the challenge of efficiently exporting and analyzing large volumes of data to meet their business requirements. Specifically, Aqua needed to export and query data at scale to share with their customers for continuous monitoring and security analysis. In this post, we explore how Aqua addressed this challenge by using aws_s3.query_export_to_s3 function with their Amazon Aurora PostgreSQL-Compatible Edition and AWS Step Functions to streamline their query output export process, enabling scalable and cost-effective data analysis.

In this post, we show you how to achieve better visibility into the health of your Amazon Aurora PostgreSQL instances, proactively address potential issues, and maintain the smooth operation of your database infrastructure. The solution is designed to scale with your deployment, providing robust and reliable monitoring for even the largest fleets of instances.

On October 22, 2024, we announced the availability of the Aurora Global Database writer endpoint, a highly available and fully managed endpoint for your global database that Aurora automatically updates to point to the current writer instance in your global cluster after a cross-Region switchover or failover, alleviating the need for application changes and simplifying routing requests to the writer instance. In this post, we dive deep into the new Global Database writer endpoint, covering its benefits and key considerations for using it with your applications.

In this post, we discuss configurations in AWS DMS endpoints and AWS DMS tasks to migrate spatial columns from Oracle to Aurora PostgreSQL-Compatible efficiently.

Vacasa is North America’s leading vacation rental management platform, revolutionizing the rental experience with advanced technology and expert teams. In the competitive short-term vacation property management industry, efficient systems are critical. To maintain its edge and continue providing top-notch service, Vacasa needed to modernize its primary transactional database to improve performance, provide high availability, and reduce costs. In this post, we share Vacasa’s journey from Amazon Relational Database Service (Amazon RDS) for MariaDB to Amazon RDS for MySQL, and finally to Amazon Aurora, highlighting the technical steps taken and the outcomes achieved.

In this post, we discuss integer sequence overflow, its causes, and—most importantly—how to efficiently set up alerts using Amazon SNS and use AWS Lambda to resolve such issues in Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL.

In this post, we focus on working with the features of mysql_fdw PostgreSQL extension on Amazon RDS for PostgreSQL to help manage a large set of data that on an external database scenarios. It enables you to interact with your MySQL database for importing individual/large/selectively number of objects at the schema level and simplifying how we get information about the MySQL/MariaDB schema, to make it easier to ultimately read/write data. We will also provide an introduction to understand query performance on foreign tables.